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实验所需的功能函数，用于空间图像计算机模拟(Function experiment, computer simulation of images for use in space )

说明：  利用最速下降法可以求得非线性方程的最优解，运算过程简单易懂。(By using the steepest descent method, the optimal solution of nonlinear equation can be obtained, and the operation process is simple and easy to understand.)

声卡采集的程序,可设置采样频率,位长,采样时间等参数.可采样8k-44.1kHz的信号,进行频谱分析.(sound card collection procedures, sampling frequency can be set up, elderly, Sampling time parameters. 8 k can be sampled- 44.1kHz signal spectrum analysis.)

（1）用Doolittle三角分解（LU）法解方程组。 （2）分别用Jacobi迭代, Gauss-Seidel迭代法解方程组。 ((1) Triangle Doolittle decomposition (LU) Solving equations. (2), respectively, with Jacobi iteration, Gauss-Seidel iteration method for solution of equations.)

基于matlab的区域生长算法，可以对图像进行分割处理。 (Matlab-based region growing algorithm to the image segmentation.)

Simple algorithm of wsn to localize mobile targets

16 点qam的调制解调simulink 文件(the modulation and demodule of QAM in simulink)

How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. (How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.)

说明：  倒立摆的控制，采用常用的PID算法，现在没有加其他的算法，或许对一些同学有用。(Inverted pendulum control, with common PID algorithm, the algorithm is not added to the other, may be useful for some students.)

说明：  simulink仿真 simulink仿真 (simulink simulation simulink simulation)